UC Berkeley

Parasitic lifestyles are the most common mode of life on earth, and their impacts on human health, agriculture, and natural systems are significant. Consistent with their pervasiveness, host-parasite interactions have not evolved in isolation, but rather in complex webs of other parasitic relationships. However, the evolutionary consequences of these ubiquitous multi-host, multi-parasite interactions have only rarely been considered. This dissertation comprises mathematical, experimental, and computational approaches to predict eco-evolutionary dynamics of multi-host, multi-parasite relationships. Moving from a pairwise understanding of host-parasite coevolution to an understanding of eco-evolutionary dynamics in a larger microbial network requires exploring several new questions, each of which formed the basis of a dissertation chapter. In Chapter 1, I examine how distinct ecological interactions among hosts influence evolutionary trajectories of their shared parasites, via a theoretical evolutionary model of a parasite infecting multiple hosts, themselves engaged in a range of mutualistic, competitive, or exploitative interactions. In Chapter 2, I conduct a multi-host experimental evolution study to understand the mechanisms by which the existence of multiple, simultaneous host pools provide unique evolutionary opportunities for bacteriophage parasites. Chapters 1 and 2 thus establish the importance of host-host interactions in driving evolution in shared parasites. In Chapter 3, I develop a broader computational framework to assess the feasibility of inferring these ecologically-informative interactions and network structures from large microbial populations sampled across space. Chapter 1: Multispecies interactions and the community context of the evolution of virulence Pairwise host-parasite relationships are typically embedded in broader networks of ecological interactions, which have the potential to shape parasite evolutionary trajectories. Understanding this ``community context" of pathogen evolution is vital for wildlife, agricultural, and human systems alike, as pathogens typically infect more than one host – and these hosts may have independent ecological relationships. Here I introduce an eco-evolutionary model examining ecological feedbacks across a range of host-host interactions. Specifically, I analyze a model of the evolution of virulence of a parasite infecting two hosts exhibiting competitive, mutualistic, or exploitative relationships. I first find that parasite specialism is necessary for inter-host interactions to impact parasite evolution. Furthermore, I find generally that increasing competition between hosts leads to higher shared parasite virulence, while increasing mutualism leads to lower virulence. In exploitative host-host interactions, the particular form of parasite specialization is critical – for instance, specialization in terms of onward transmission, host tolerance, or intra-host pathogen growth rate lead to distinct evolutionary outcomes under the same host-host interactions. This work provides testable hypotheses for multi-host disease systems, predicts how changing interaction networks may impact virulence evolution, and broadly demonstrates the importance of looking beyond pairwise relationships to understand evolution in realistic community contexts. Chapter 2: Coevolution with competing bacterial hosts opens unique evolutionary pathways for bacteriophage Viral bacteriophage parasites often have multiple bacterial hosts; these hosts vary in susceptibility and temporal availability, creating a dynamic tapestry upon which phage-host coevolution plays out. For multi-host phages, it is unclear whether complex host communities will constrain phage evolution due to the imposition of a diverse set of tradeoffs, or if a more cosmopolitan coevolutionary process will drive evolutionary novelty. To explore contexts in which multi-host coevolution constrains or expands phage coevolutionary trajectories, I coevolved a total of 72 replicate lines of lytic dsDNA phage M3.1 with a single or two-host pool of Pseudomonas syringae pv. tomato strains (DC3000 and PT23). Experimental coevolution was conducted in two nutrient conditions that alter relative host susceptibility and underlying host metabolism. I tracked host and phage population dynamics, assayed evolved phage virulence on ancestral hosts, and whole-genome sequenced evolved phage populations to better understand the genetic basis of adaptation during single- vs multi-host coevolution. I find that, despite predominantly transient host coexistence, multi-host coevolution facilitated more stable phage populations and innovation in genes implicated in nucleotide metabolism, providing valuable insight into the community-dependent mechanisms that drive evolution in distinct regions of lytic phage genomes. The degree to which these multi-host coevolutionary trajectories facilitated arms race dynamics, in which evolved phages were more virulent than ancestral phages on naive hosts, or a non-monotonic relationship between coevolutionary time and virulence on ancestral hosts (either due to fluctuating selection dynamics or an emergent tradeoff between infectivity on contemporary and ancestral hosts), was environment-dependent. Understanding the mechanisms by which host diversity, including diversity in response to the abiotic environment, shape generalist phage evolution is particularly relevant for the design of phage therapeutics, which must consider how phage eco-evolutionary dynamics will be shaped by interactions with hosts beyond the target bacterial pathogen. Chapter 3: Co-occurrence networks can preserve emergent properties of ecological communities Interaction networks, in which nodes represent species and edges represent direct interactions between species, have a long and impactful history in community ecology. However, co-occurrence networks, where edges represent statistical relationships among species presences or abundances, are often easier to construct from lab and field data. It is clear that co-occurrence edges often do not represent direct interactions, but frameworks for the interpretation of co-occurrence networks have not kept pace with their generation. It is therefore unclear when and how these networks can be used to gain insight into community dynamics. Here, I use a Generalized Lotka-Volterra-based model to explore the contexts in which emergent properties of species interaction networks are identifiable in their resulting co-occurrence networks. I find that, in spite of many differences in direct edges, key features of the true interaction network, such as unipartite modularity, high-degree nodes (hubs), and bipartite modularity and nestedness, can be preserved in co-occurrence networks. In contrast, node degree distributions are not preserved even in the most idealized scenarios. I propose that networks derived from large co-occurrence datasets could therefore be used in future empirical work to test existing hypotheses of how emergent network structures drive ecological community dynamics.